Visualizing Multimodal Deep Learning for Lesion Prediction
A U-Net is a type of convolutional neural network that has been shown to output impressive results in medical imaging segmentation tasks. Still, neural networks in general form a black box that is hard to interpret, especially by noncomputer scientists. This work provides a visual system that allows...
| Publié dans: | IEEE computer graphics and applications. - 1991. - 41(2021), 5 vom: 31. Sept., Seite 90-98 |
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| Auteur principal: | |
| Autres auteurs: | , , , , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2021
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| Accès à la collection: | IEEE computer graphics and applications |
| Sujets: | Journal Article |
| Résumé: | A U-Net is a type of convolutional neural network that has been shown to output impressive results in medical imaging segmentation tasks. Still, neural networks in general form a black box that is hard to interpret, especially by noncomputer scientists. This work provides a visual system that allows users to examine U-Nets that were trained to predict brain lesions caused by stroke using multimodal imaging. We provide several visualization views that allow users to load trained U-Nets, run them on different patient data, and examine the results while visually following the computation of the U-Net. With these visualizations, we can provide useful information for our medical collaborators showing how the training database can be improved and which features are best learned by the neural network |
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| Description: | Date Completed 27.09.2021 Date Revised 27.09.2021 published: Print Citation Status PubMed-not-MEDLINE |
| ISSN: | 1558-1756 |
| DOI: | 10.1109/MCG.2021.3099881 |